Self-managing chronic conditions typically involves a diverse network of individuals and devices, forming a self-management ecosystem. For this ecosystem to be effective, components need to work together cohesively. The rapid advancement of technology means new devices need to be repeatedly integrated into existing self-management ecosystems. To examine this process, we used the case study of young adults with type 1 diabetes (T1D) in the UK who were given a smartwatch. Over six months, interviews and focus groups were performed to explore their smartwatch use alongside T1D management. Thematic analysis highlighted that smartwatches have potential as a display, interface and data source in T1D management, which is of particular importance as artificial intelligence plays a growing role in self-management ecosystems. It also emphasised the need for customisation, flexibility and adaptability, and automation in the design of technology to promote integration into existing self-management ecosystems for both T1D and other chronic conditions.

Reinforcement learning (RL) holds promise for supporting personalised decision-making in healthcare, but existing approaches often struggle to incorporate patient expertise and individual preferences, key components for clinically viable AI systems. This work introduces PAINT (Preference Adaptation for Individualised Treatment), a general framework for preference-guided offline RL in safety-critical settings. PAINT combines sketch-based reward annotation with safety-constrained policy optimisation, enabling fine-grained preference capture from historical patient data without requiring action labels. A reward model trained on this feedback guides offline RL while supporting tunable sensitivity to preference signals and enforcing clinical safety constraints. Using type 1 diabetes (T1D) management as a case study, in-silico evaluation with the FDA-accepted T1D simulator demonstrates that can PAINT reduces patient risk by 15% over commercial baselines under guidance, while enabling preference-driven adaptations such as improved management during challenging mealtime events and enhanced robustness to dosing errors. The method further shows resilience to real-world challenges including sample size, annotation noise, and inter-patient variability. These findings suggest PAINT offers a practical pathway for integrating human feedback into offline RL in patient settings, with broader implications for developing trustworthy and adaptive AI systems in healthcare.

The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL for developing effective dosing policies without the need for potentially dangerous patient interaction during training. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of the 30 virtual patients available within the FDA-approved UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the total training samples required by online RL to achieve stable performance, this work shows that offline RL can significantly increase time in the healthy blood glucose range from 61.6±0.3% to 65.3±0.5% when compared to the strongest state-of-art baseline (p<0.001). This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging control scenarios such as incorrect bolus dosing, irregular meal timings and compression errors. The code for this work is available at: https://github.com/hemerson1/offline-glucose.